Automated Object Classification Using Temperature Profiles

ABSTRACT

Methods and apparatus are provided for automated object classification using temperature profiles. An object in an environment (such as an exemplary data center) is classified by obtaining a surface temperature profile of the object; and classifying the object as a particular type of equipment based on the obtained surface temperature profile. The surface temperature profile of the object can be compared to a plurality of predefined characteristic surface temperature profiles each associated with a given type of equipment.

FIELD OF THE INVENTION

The present invention relates to automated techniques for automaticallyclassifying objects, such as equipment in a data center or anotherenvironment.

BACKGROUND OF THE INVENTION

Data centers house information technology (IT) equipment and otherobjects, such as servers, telecommunication systems and storage systems.This IT equipment is normally housed in racks. Data centers also containsecondary equipment to keep the IT equipment functioning, and, moreover,functioning under acceptable environmental conditions, such asacceptable ranges of heat and humidity. This secondary equipment isoften referred to as “facilities equiment” and includes powerdistribution units (PDUs), computer room air conditioners (CRACs) anduninterruptable power supplies. It is often challenging to locate andclassify the equipment in a large computing facility or data center.

When a computer data center is built or goes into service there is oftenan accompanying layout document or blueprint that identifies thelocation and type of each piece of equipment in the center. Thisdocument is often created using a Computer Aided Design (CAD) tool orsimilar software. The layout shows the initial location of equipment atthe time the data center was established and is typically updated onlyafter a major redesign of the center. Such drawings are rarely updatedas equipment is replaced and any of a myriad of minor layout changes aremade to the center. Maintaining such a document is cumbersome and istherefore often neglected.

A number of techniques have been proposed or suggested for employing oneor more robots to automatically navigate, map and monitor data centers.For example, J. Lenchner et al., “Towards Data Center Self-DiagnosisUsing a Mobile Robot,” ACM Int'l Conf. on Autonomic Computing (ICAC '11)(2011), incorporated by reference herein, discloses a robot that servesas a physical autonomic element to automatically navigate, map andmonitor data centers. The disclosed robot navigates a data center,mapping its layout and monitoring its temperature and other quantitiesof interest with little, if any, human assistance. In addition, U.S.patent application Ser. No. 12/892,532, filed Sep. 28, 2010, entitled“Detecting Energy and Environmental Leaks in Indoor Environments Using aMobile Robot,” incorporated by reference herein, discloses techniquesfor energy and environmental leak detection in an indoor environmentusing one or more mobile robots.

While the use of robots has greatly improved the ability toautomatically monitor indoor environments, they suffer from a number oflimitations, which if overcome, could further extend the utility andefficiency of robots that are monitoring an indoor environment. Forexample, the robots are unable to automatically classify the type ofdata center equipment.

A number of tagging techniques have been proposed or suggested toautomatically classify the location and/or type of data centerequipment. For example, barcodes and RFID tags have been used to allowequipment to be automatically identified. Barcodes and RFID tags,however, require that the equipment be previously tagged with a barcodeand/or an RFID tag—a process that is labor intensive and henceexpensive.

A need remains for automated techniques for identifying, locating and/orclassifying objects, such as equipment in a data center or anotherenvironment. Yet another need remains for automated techniques foridentifying, locating and/or classifying objects without having to tagthe equipment in some way.

SUMMARY OF THE INVENTION

Generally, methods and apparatus are provided for automated objectclassification using temperature profiles. According to one aspect ofthe invention, an object in an environment (such as an exemplary datacenter) is classified by obtaining a surface temperature profile of theobject; and classifying the object as a particular type of equipmentbased on the obtained surface temperature profile. The surfacetemperature profile of the object can be compared to a plurality ofpredefined characteristic surface temperature profiles each associatedwith a given type of equipment.

The predefined characteristic surface temperature profiles can bestored, for example, as rules and/or feature vectors. The predefinedcharacteristic surface temperature profiles associated with a given typeof equipment can optionally be learned over time using human-labeledexamples.

The predefined characteristic surface temperature profiles evaluatetemperature characteristics of the object, such as a bottom-to-toptemperature gradient of the object, a temperature of the object at oneor more air inlets and a temperature of the object at one or more airoutlets. The predefined characteristic surface temperature profiles canoptionally compare a surface temperature of the object to an airtemperature. In a further variation, the predefined characteristicsurface temperature profiles can evaluate an approximate heightpopulated with equipment that is powered on. A further classificationcriterion can be based on a physical extent of the object.

The surface temperature profile of the object can be obtained, forexample, using a robot having at least one temperature measuring device,and/or a plurality of infrared imaging devices. The robot optionallyfurther comprises a location sensing capability.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary indoor environment (e.g., a data center)in which the present invention can be employed;

FIG. 2 is a flow chart describing an exemplary implementation of anexemplary data center asset classification process; and

FIG. 3 is a block diagram of a robot navigation system that canimplement the processes of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention provides automated techniques for identifying.locating and/or classifying objects, such as equipment in a data centeror another environment. For example, data centers house informationtechnology (IT) equipment housed in racks in addition to secondary“facilities equipment” to keep the IT equipment functioning, and,moreover, functioning under acceptable environmental conditions.

The present invention recognizes that many of these types of objectshave distinguishing surface temperature profiles. The surfacetemperature profile is evident upon inspection with a thermal measuringdevice, such as an infrared thermometer or an infrared camera. Accordingto one aspect of the invention, a data center monitoring robot isoutfitted with an infrared sensing capability for classifying the objecttype (e.g., equipment type) and location sensing capability fordetermining the location of the object. In this manner, the data centermonitoring robot can classify such object and also determine theposition of the classified equipment. In one variation, a configurationof deployed infrared cameras can be employed to classify and locateobjects in a data center or another environment.

The present invention recognizes, for example, that a Computer Room AirConditioning unit is similar to a refrigerator and is cold to the touch.Although a CRAC has a detectable bottom-to-top temperature gradient(increasing temperature with height), a CRAC is uniformly cold at itsbottom (in other words, cold along all of its bottom sides), as opposedto a rack of servers or other IT equipment, which is typically cold onlyat the air inlet sides, especially if the IT equipment is situated (asis typical) with its inlet side facing perforated tiles, through whichcool air is vented.

Power Distribution Units (or PDUs) may be warm to the touch but PDUs areoften well insulated and do not have a noticeably different surfacetemperature than walls. PDUs, however, can be distinguished from roomwalls in that they are three dimensional (having a substantial thicknessor depth) and do not form the boundary of a room. If interior to a room,PDUs are not as thin, narrow and long as walls. PDUs can bedistinguished from pillars or columns in that they generally do notextend to the ceiling and are typically more than a couple of feet wide.

According to a further aspect of the invention, the approximate heightto which racks are populated with equipment or other objects (with thepower turned on) can be identified, since temperatures inside the rackson both the intake and exhaust sides of the racks will generally plateauat such points. A temperature plateau develops because a relativelymotionless air mass tends to accumulate inside of the racks at and abovesuch elevations, with no air being actively drawn into the inlet sideand no air being expelled from the outlet side.

As discussed hereinafter, in an exemplary implementation, a robot candetermine its location within an environment, for example, using acomputer vision system, or another Simultaneous Localization and Mapping(SLAM) system, such as one based on laser range finding. In addition, inaccordance with the present invention, the robot can be deployed with aninfrared sensor that is directed at all obstacles to obtain an infraredprofile, such as a top-to-bottom infrared profile.

As discussed hereinafter, each time a robot encounters an object, thesurface temperature profile of the detected object is evaluated todetermine if the object can be classified as a particular type ofequipment based on its surface temperature profile. In one variation, ifthe object cannot be classified as a particular type of equipment basedon its surface temperature profile, then the object is optionallyfurther evaluated using a vision or laser system to determine thephysical extent of the object to differentiate between certain types ofequipment and walls or other obstacles.

The characteristic surface temperature profiles can be recorded, forexample, in the form of one or more rules within a rule base or aprocess or in one or more feature vectors. The characteristic surfacetemperature profiles may be manually created/entered by a human, orautomatically learned by a learning system using human-labeled examples.For example, pictures of assets from a data center can be presented to ahuman for classification. Thereafter, the learning system can, forexample, form clusters of associated temperatures andfront-to-back/bottom-to-top temperature gradients based on thehuman-assigned asset type. When a new, unknown asset is encountered, thesystem can then find the closest matching cluster and assign the assettype based on the associated cluster.

The term “building,” as used herein, is intended to refer to a varietyof facilities, including, but not limited to, data centers,manufacturing facilities industrial office space, and residentialbuildings.

As previously indicated, in one exemplary implementation, a robot isoutfitted with an infrared sensing capability for classifying the typeof equipment and location sensing capability for determining thelocation of the equipment. For a detailed discussion of suitableexemplary robots, see, for example, U.S. patent application Ser. No.12/892,532, filed Sep. 28, 2010, entitled “Detecting Energy andEnvironmental Leaks in Indoor Environments Using a Mobile Robot,” and/orU.S. patent application Ser. No. 13/348,846, filed Jan. 12, 2012,entitled “Discovery and Monitoring of an Environment Using a Pluralityof Robots,” each incorporated by reference herein.

The term “robot,” as used herein, refers generally to any form of mobileelectro-mechanical device that can be controlled by electronic orcomputer programming. In this basic form, as will be described in detailbelow, the exemplary robots move throughout the designated portions ofthe building 100 and take temperature, air flow and/or airborne mattermeasurements as well as time and positioning data (so as to permit thetemperature, air flow and/or airborne matter data to be associated witha given position in the building 100 at a particular time). The robotsshould be capable of moving in various directions along the floor of thebuilding, so as to navigate where the robots need to go and to maneuveraround obstacles, such as equipment, furniture, walls, etc. in thebuilding 100.

In one variation of the invention, a configuration of deployed infraredcameras can be employed to classify and locate equipment in a datacenter or another environment, without the use of robots.

FIG. 1 illustrates an exemplary indoor environment 100, such as a datacenter, in which the present invention can be employed. While thepresent invention is illustrated in the context of an exemplary datacenter, the present invention can be employed in any environment havingequipment with significantly varying surface temperature profiles, suchas a manufacturing environment where the manufacturing equipment hascharacteristic surface temperature profiles, as would be apparent to aperson of ordinary skill in the art.

The exemplary indoor environment 100 of FIG. 1 comprises an exemplaryarray of 12-by-12 tiles. The exemplary data center comprises three racks110-1 through 100-3 of information technology equipment, two computerroom air conditioners (CRACs) 120-1 and 120-2, a power distribution unit(PDU) 130, walls 140, comprising the outer boundary of the center, and asingle pillar 150.

FIG. 2 is a flow chart describing an exemplary implementation of anexemplary data center asset classification process 200 incorporatingaspects of the present invention. As shown in FIG. 2, during step 210 ofthe exemplary data center asset classification process 200, a robotbegins mapping a computer data center using some form of SLAM(Simultaneous Localization and Mapping).

When the robot encounters an obstacle during step 220, the robot pointsits infrared sensor(s) or camera at the obstacle to obtain atop-to-bottom temperature profile of the detected object. A test isperformed during step 230 to determine if the object has a surfacetemperature, especially near the bottom, that is at least 10 degreesFahrenheit (F) lower than the nearby air temperature. If it isdetermined during step 230 that the object has a surface temperaturethat is at least 10 degrees F. lower than the nearby air temperature,then the object is classified as a CRAC during step 240. Thereafter, theexemplary data center asset classification process 200 returns to step220 to continue mapping other portions of the environment 100.

If, however, it is determined during step 230 that that the object doesnot have a surface temperature that is at least 10 degrees F. lower thanthe nearby air temperature, then a further test is performed during step250 to determine if the obstacle extends from floor to ceiling. If it isdetermined during step 250 that the object extends from floor toceiling, then a further test is performed during step 260 to determineif the obstacle has a self-contained extent, not surrounding any space(i.e., in the sense that the wall 140 around a room surrounds space).

If it is determined during step 260 that the object has a self-containedextent, not surrounding any space, then the object is classified duringstep 265 as a pillar. Thereafter, the exemplary data center assetclassification process 200 returns to step 220 to continue mapping otherportions of the environment 100.

If, however, it is determined during step 260 that the object does nothave a self-contained extent, then the object is classified during step270 as a wall. Thereafter, the exemplary data center assetclassification process 200 returns to step 220 to continue mapping otherportions of the environment 100.

If it was determined during step 250 that the object does not extendfrom floor to ceiling, then a further test is performed during step 280to determine if the obstacle has at least 10 degrees F. of asymmetry inits front-to-back heat distribution or have perforations on its front orback doors. If it is determined during step 280 that the object has atleast 10 degrees F. of asymmetry in its front-to-back heat distributionor has perforations on its front or back doors, then the object isclassified during step 285 as a rack. Thereafter, the exemplary datacenter asset classification process 200 returns to step 220 to continuemapping other portions of the environment 100.

If, however, it is determined during step 280 that the object does nothave at least 10 degrees F. of asymmetry in its front-to-back heatdistribution or perforations on its front or back doors, then the objectis classified during step 290 as a PDU. Thereafter, the exemplary datacenter asset classification process 200 returns to step 220 to continuemapping other portions of the environment 100.

According to an exemplary embodiment, each robot also has a visioncomponent, e.g., a mounted camera. In the context of a regularly gridded(e.g., tiled) room such as a data center, the vision component of therobot is responsible for detecting a “pose” of the robot with respect tothe center of a tile, and for determining whether the next tile whichthe robot wishes to investigate is visitable or blocked (for example,because the tile is occupied by equipment or otherwise obstructed). Thepose of the robot is the location and orientation of the robot relativeto the forward pointing “orthogonal” orientation at the center of thetile. The forward pointing orthogonal orientation is the orientationthat is exactly aligned with the intended reckoning of the robot (fromthe center of one tile to the center of a second adjacent tile) suchthat if the robot moved straight ahead it would cross the boundarybetween the tiles along a path which is perpendicular (orthogonal) tothe tile boundary and reach the center of the second tile in which iteither intends to get to or intends to inspect, with the purpose ofdetermining whether the second tile is visitable. This assumes that a(theoretical) straight line connecting the centers of two adjacent tilesis perpendicular (orthogonal) to the boundary between the two tiles,which is typically the case in data centers and many other indoorenvironments.

In the data center context, the vision component specializes indetecting tile boundaries, determining a distance of the robot from atile boundary (and thereby, a distance of the robot from the center ofthe tile), determining an angle the robot currently makes with the lineorthogonal to the next tile boundary, and determining whether the nexttile in the direction the robot is headed is occupied or visitable.According to an exemplary embodiment, the robot automaticallydetermines, e.g., tile boundaries and whether a tile is visitable orobstructed. The programming of the robot to perform this task would beapparent to one of skill in the art and thus is not described furtherherein. For orientation purposes, the robot has a forward-pointingdirection determined by the direction in which the vision component,e.g., camera, faces. This forward-pointing direction is also alignedwith a forward wheel direction when the robot is instructed to moveforward (i.e., when the robot rotates, it is not just the wheels thatrotate but the entire assembly).

In a more general facility where there is no guarantee of a griddedlayout of tiles the robot can use methods of simultaneous localizationand mapping (SLAM) not based entirely on vision, but in addition, orinstead, based on laser range finding, sonar, detecting reflectedpatterned infrared light, or other methods.

While FIG. 2 shows an exemplary sequence of steps, it is also anembodiment of the present invention that these sequences may be varied.Various permutations of the algorithms are contemplated as alternateembodiments of the invention.

While exemplary embodiments of the present invention have been describedwith respect to processing steps in a software program, as would beapparent to one skilled in the art, various functions may be implementedin the digital domain as processing steps in a software program, inhardware by a programmed general-purpose computer, circuit elements orstate machines, or in combination of both software and hardware. Suchsoftware may be employed in, for example, a hardware device, such as adigital signal processor, application specific integrated circuit,micro-controller, or general-purpose computer. Such hardware andsoftware may be embodied within circuits implemented within anintegrated circuit.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore. aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIG. 3 is a block diagram of an equipment classification system 300 thatcan implement the processes of the present invention. As shown in FIG.3. memory 330 configures the processor 320 to implement the robotnavigation and equipment classification methods, steps, and functionsdisclosed herein (collectively, shown as 380 in FIG. 3). The memory 330could be distributed or local and the processor 320 could be distributedor singular. The memory 330 could be implemented as an electrical,magnetic or optical memory, or any combination of these or other typesof storage devices. It should be noted that each distributed processorthat makes up processor 320 generally contains its own addressablememory space. It should also be noted that some or all of computersystem 300 can be incorporated into a personal computer, laptopcomputer, handheld computing device, application-specific circuit orgeneral-use integrated circuit.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

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 15. (canceled)16. An apparatus for classifying an object in an environment, theapparatus comprising: a memory; and at least one hardware device,coupled to the memory, operative to: obtain a surface temperatureprofile of said object; and classify said object as a particular type ofequipment based on said obtained surface temperature profile.
 17. Theapparatus of claim 16, wherein said at least one hardware device isfurther configured to compare said surface temperature profile of saidobject to a plurality of predefined characteristic surface temperatureprofiles each associated with a given type of equipment.
 18. Theapparatus of claim 17, wherein said predefined characteristic surfacetemperature profiles are stored as one or more of rules and featurevectors.
 19. The apparatus of claim 17, wherein said predefinedcharacteristic surface temperature profiles evaluate temperaturecharacteristics of said object.
 20. The apparatus of claim 19, whereinsaid temperature characteristics of said object comprise one or more ofa bottom-to-top temperature gradient of said object, a temperature ofsaid object at one or more air inlets and a temperature of said objectat one or more air outlets.
 21. The apparatus of claim 16, wherein saidat least one hardware device is further configured to evaluate aphysical extent of said object and wherein said classification isfurther based on said physical extent.
 22. The apparatus of claim 16,wherein said at least one hardware device is further configured todetermine a location of said object.
 23. The apparatus of claim 16,wherein said environment comprises one or more of a data center and amanufacturing environment.
 24. The apparatus of claim 17, wherein saidplurality of predefined characteristic surface temperature profilesassociated with a given type of equipment is learned over time usinghuman-labeled examples.
 25. An article of manufacture for classifying anobject in an environment, comprising a tangible machine readablerecordable medium containing one or more programs which when executedimplement the steps of: obtaining a surface temperature profile of saidobject; and classifying said object as a particular type of equipmentbased on said obtained surface temperature profile.
 26. The article ofmanufacture of claim 25, further comprising the step of comparing saidsurface temperature profile of said object to a plurality of predefinedcharacteristic surface temperature profiles each associated with a giventype of equipment.
 27. The article of manufacture of claim 26, whereinsaid predefined characteristic surface temperature profiles are storedas one or more of rules and feature vectors.
 28. The article ofmanufacture of claim 26, wherein said predefined characteristic surfacetemperature profiles evaluate temperature characteristics of saidobject.
 29. The article of manufacture of claim 28, wherein saidtemperature characteristics of said object comprise one or more of abottom-to-top temperature gradient of said object, a temperature of saidobject at one or more air inlets and a temperature of said object at oneor more air outlets.
 30. The article of manufacture of claim 26, whereinsaid predefined characteristic surface temperature profiles compare asurface temperature of said object to an air temperature.
 31. Thearticle of manufacture of claim 26, wherein said predefinedcharacteristic surface temperature profiles evaluate an approximateheight populated with equipment that is powered on.
 32. The article ofmanufacture of claim 25, further comprising the step of evaluating aphysical extent of said object and wherein said classifying step isfurther based on said physical extent.
 33. The article of manufacture ofclaim 25, further comprising the step of determining a location of saidobject.
 34. The article of manufacture of claim 33, wherein saidlocation of said object is determined using a Simultaneous Localizationand Mapping system.
 35. The article of manufacture of claim 25, whereinsaid surface temperature profile of said object is obtained using arobot having at least one temperature measuring device.
 36. The articleof manufacture of claim 35, wherein said robot further comprises alocation sensing capability.
 37. The article of manufacture of claim 25,wherein said surface temperature profile of said object is obtainedusing a plurality of infrared imaging devices.
 38. The article ofmanufacture of claim 25, wherein said environment comprises one or moreof a data center and a manufacturing environment.
 39. The article ofmanufacture of claim 26, wherein said plurality of predefinedcharacteristic surface temperature profiles associated with a given typeof equipment is learned over time using human-labeled examples.